Queries · q184 · Director of Fulfilment Operations · week 1, night 1

Scale of stock count discrepancies by month

“How often do physical cycle counts come back different from the stock quantity we had on record, and how large are those differences in units and estimated value, across 2025?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. A business event lands on the Director of Fulfilment Operations's desk

   Week 1; shared with Tobias Lindqvist, Chief Financial Officer

   Auditors and merchants question inventory accuracy. How often do counts, adjustments and reservations disagree with recorded stock, where, and why?

   One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events

2. Tomasz Nowak writes to Fatima Zahra

   The manager, at the start of week 1

   Subject: Inventory accuracy — what I need for Friday's memo

   Fatima,

   Tobias and I need to give the auditors (and our bigger merchants) a credible answer on how trustworthy our stock numbers are, where it breaks down, and whether it's getting better or worse. This feeds the joint audit response and will likely shape where we invest in warehouse process fixes next quarter — so I'd rather have three sharp answers than ten partial ones. Please use your nightly slots on these, in rough priority order:

   1. How big is the gap between what we think we have and what's actually there? Across our warehouses, how often do physical counts come back different from the stock we had on record, and how large are those differences in units (and ideally value)? I want a sense of scale before anything else.
   2. Where is it worst? Does the mismatch concentrate in particular warehouses, or particular kinds of storage locations (e.g. bulk shelving vs. pick bins)? If a handful of sites or location types account for most of the problem, that tells us where to send help first.
   3. Is it a product or a process problem? Are the discrepancies spread evenly across products, or concentrated in certain categories, high-velocity items, or items we handle in lots/serials? This will tell Tobias and me whether this is a handling issue or a tracking-method issue.
   4. Are our "soft holds" on stock reliable? When we reserve stock for an order, does that reservation actually get fulfilled as expected, or do we see a lot of stuck/expired holds that quietly overstate what's available to sell? This matters for whether we're overpromising stock to customers.
   5. Is this getting better or worse over time? A simple trend over the last several months would tell us whether recent process changes are helping or whether the problem is growing — useful context for the auditors either way.

   If you can only get to three this week, do 1, 2 and 3 — those are the ones Tobias and I will lean on most heavily in the audit conversation. Happy to talk through trade-offs before you lock in Monday night's requests.

   Thanks, Tomasz

   Show the whole note
   A note from the Chief Financial Officer, Tobias Lindqvist

   Subject: Finance questions for the inventory accuracy review

   Hi Fatima,

   A few things from the finance side as you dig into this with Tomasz:

   1. Do stock discrepancies cluster around particular merchants or warehouses in a way that could point to a write-off or valuation exposure we need to flag in the annual accounts?
   2. When counts don't match recorded stock, is it skewed more toward unexplained losses (shrinkage) versus unexplained gains — and has that pattern shifted over the year? That matters for how we size a shrinkage provision.
   3. Are the mismatches concentrated in slower-moving or higher-value goods, or spread evenly? That shapes how material this is for the merchants we'd flag to auditors.

   Keep it high level for now — happy to go deeper once you have an initial read.

   Thanks, Tobias

3. Fatima Zahra plans the night

   The analyst · week 1, night 1, request 1 of 3

   Tomasz was explicit: if only three fit this week, do his #1 (scale), #2 (where it concentrates), #3 (product vs process). I'm doing exactly those three tonight, and folding Tobias's overlapping asks into the same reports rather than spending extra slots: merchant clustering → tenant column in report 2 (each warehouse belongs to one tenant); shrinkage-vs-gain skew → signed net variance columns in reports 1 & 2; velocity/value materiality → velocity and value columns in report 3. I'm deferring Tomasz's #4 (reservation reliability) and #5 (dedicated trend) to a later night, though report 1's month grain gives a free first look at trend.

   Definition check before committing: "physical count different from stock on record" should be measured from stock_movements where kind = 'cycle_count' — that's an enumerated, reliable code — rather than stock_adjustments.reason, which is free text with no fixed code list and reasons like "damage"/"shrinkage" aren't cycle-count-driven surprises, they're logged causes. I don't want the engineer guessing at string matches, so I'm specifying stock_movements.kind='cycle_count' as the source of truth for all three reports, and treating "count found less than system" (quantity<0) as shrinkage and "found more" (quantity>0) as overage.

   Value is costed off supplier_products.unit_cost (preferred supplier, else cheapest active) rather than retail price, since write-off/valuation exposure (Tobias's framing) is a cost-basis question. All money converted to EUR via exchange_rates (base_code=EUR): value_eur = value_in_quote_currency / rate.

   All three reports use the same 12-month window (2025-01-01 to 2025-12-31, the last complete year) so they're comparable to each other.

   Read the whole plan
   Scale of stock count discrepancies by month

   Question

   How often do physical cycle counts come back different from the stock quantity we had on record, and how large are those differences in units and estimated value, across 2025?

   One row per

   calendar month (Jan 2025 - Dec 2025)

   Columns

   • month (YYYY-MM)
   • cycle_count_events (count of stock_movements rows with kind='cycle_count')
   • warehouses_affected (distinct warehouses with at least one cycle_count event that month)
   • units_short (sum of abs(quantity) where quantity<0, i.e. counted less than recorded)
   • units_over (sum of quantity where quantity>0, i.e. counted more than recorded)
   • net_units_variance (sum of signed quantity; negative = net shrinkage)
   • abs_units_variance (sum of abs(quantity), total magnitude of mismatch)
   • estimated_value_eur (sum of abs(quantity) * unit cost, converted to EUR)
   • avg_abs_units_per_event (abs_units_variance / cycle_count_events, rounded 1dp)

4. Omar writes the SQL

   The engineer · 3 dry runs on the empty schema, then the submission

   Dry run 1 · STATUS: REJECTED

   with cc as (
     select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id
     from stock_movements sm
     join stock_locations sl on sl.id = sm.stock_location_id
     where sm.kind = 'cycle_count'
       and sm.occurred_at >= timestamp '2025-01-01'
       and sm.occurred_at < timestamp '2026-01-01'
   ),
   supplier_cost as (
     select variant_id, currency_code, unit_cost
     from (
       select variant_id, currency_code, unit_cost,
              row_number() over (
                partition by variant_id
                order by case when is_preferred then 0 else 1 end asc, unit_cost asc
              ) as rn
       from supplier_products
       where is_preferred = true or is_active = true
     ) t
     where rn = 1
   ),
   valued as (
     select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,
            sc.currency_code, sc.unit_cost, er.rate
     from cc
     left join supplier_cost sc on sc.variant_id = cc.variant_id
     left join exchange_rates er
       on er.base_code = 'EUR'
      and er.quote_code = sc.currency_code
      and er.as_of = cast(cc.occurred_at as date)
   )
   select
     to_char(occurred_at, 'YYYY-MM') as month,
     count(*) as cycle_count_events,
     count(distinct warehouse_id) as warehouses_affected,
     coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
     coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
     sum(quantity) as net_units_variance,
     sum(abs(quantity)) as abs_units_variance,
     round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,
     round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event
   from valued
   group by to_char(occurred_at, 'YYYY-MM')
   order by month

   Dry run 2 · 3 changes from the dry run before · STATUS: REJECTED

   with cc as (  select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id  from stock_movements sm  join stock_locations sl on sl.id = sm.stock_location_id  where sm.kind = 'cycle_count'    and sm.occurred_at >= timestamp '2025-01-01'    and sm.occurred_at < timestamp '2026-01-01'),supplier_cost as (  select variant_id, currency_code, unit_cost  from (    select variant_id, currency_code, unit_cost,    select sp.variant_id, sp.currency_code, sp.unit_cost,           row_number() over (             partition by variant_id             order by case when is_preferred then 0 else 1 end asc, unit_cost asc             partition by sp.variant_id             order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc           ) as rn    from supplier_products    where is_preferred = true or is_active = true    from supplier_products sp    join suppliers s on s.id = sp.supplier_id    where sp.is_preferred = true or s.is_active = true  ) t  where rn = 1),valued as (  select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,         sc.currency_code, sc.unit_cost, er.rate  from cc  left join supplier_cost sc on sc.variant_id = cc.variant_id  left join exchange_rates er    on er.base_code = 'EUR'   and er.quote_code = sc.currency_code   and er.as_of = cast(cc.occurred_at as date))select  to_char(occurred_at, 'YYYY-MM') as month,  count(*) as cycle_count_events,  count(distinct warehouse_id) as warehouses_affected,  coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,  coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,  sum(quantity) as net_units_variance,  sum(abs(quantity)) as abs_units_variance,  round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,  round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_eventfrom valuedgroup by to_char(occurred_at, 'YYYY-MM')order by month

   Dry run 3 · 2 changes from the dry run before · STATUS: COMPILES (dry run on the empty schema, no data read)

   with cc as (  select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id  from stock_movements sm  join stock_locations sl on sl.id = sm.stock_location_id  where sm.kind = 'cycle_count'    and sm.occurred_at >= timestamp '2025-01-01'    and sm.occurred_at < timestamp '2026-01-01'),supplier_cost as (  select variant_id, currency_code, unit_cost  from (    select sp.variant_id, sp.currency_code, sp.unit_cost,           row_number() over (             partition by sp.variant_id             order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc           ) as rn    from supplier_products sp    join suppliers s on s.id = sp.supplier_id    where sp.is_preferred = true or s.is_active = true  ) t  where rn = 1),valued as (  select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,         sc.currency_code, sc.unit_cost, er.rate  from cc  left join supplier_cost sc on sc.variant_id = cc.variant_id  left join exchange_rates er    on er.base_code = 'EUR'   and er.quote_code = sc.currency_code   and er.as_of = cast(cc.occurred_at as date))select  to_char(occurred_at, 'YYYY-MM') as month,  strftime(occurred_at, '%Y-%m') as month,  count(*) as cycle_count_events,  count(distinct warehouse_id) as warehouses_affected,  coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,  coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,  sum(quantity) as net_units_variance,  sum(abs(quantity)) as abs_units_variance,  round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,  round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_eventfrom valuedgroup by to_char(occurred_at, 'YYYY-MM')group by strftime(occurred_at, '%Y-%m')order by month

   “Query compiles and the output columns match the requested report exactly.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. One thing worth flagging to Fatima: supplier_products itself has no is_active flag — I treated "active supplier_products rows" as rows coming from a supplier with suppliers.is_active = true, which is the closest match in the schema. That's noted in the submit note as well.”

5. The report comes back overnight

   As Fatima Zahra saw it, from the small dataset the agents worked on

   month   | cycle_count_events | warehouses_affected | units_short | units_over | net_units_variance | abs_units_variance | estimated_value_eur | avg_abs_units_per_event
   --------+--------------------+---------------------+-------------+------------+--------------------+--------------------+---------------------+------------------------
   2025-01 |                764 |                  85 |         510 |        254 |               -256 |                764 |             20348.0 |                     1.0
   2025-02 |                760 |                  90 |         515 |        245 |               -270 |                760 |             19274.0 |                     1.0
   2025-03 |                822 |                  90 |         579 |        243 |               -336 |                822 |             21343.0 |                     1.0
   2025-04 |                783 |                  93 |         521 |        262 |               -259 |                783 |             20197.0 |                     1.0
   2025-05 |                853 |                  94 |         605 |        248 |               -357 |                853 |             25375.0 |                     1.0
   2025-06 |                818 |                  98 |         542 |        276 |               -266 |                818 |             19756.0 |                     1.0
   2025-07 |                849 |                 101 |         580 |        269 |               -311 |                849 |             23154.0 |                     1.0
   2025-08 |                865 |                 102 |         588 |        277 |               -311 |                865 |             21544.0 |                     1.0
   2025-09 |                875 |                 110 |         608 |        267 |               -341 |                875 |             22525.0 |                     1.0
   2025-10 |                941 |                 105 |         667 |        274 |               -393 |                941 |             25539.0 |                     1.0
   2025-11 |                917 |                 111 |         650 |        267 |               -383 |                917 |             21766.0 |                     1.0
   2025-12 |                946 |                 108 |         671 |        275 |               -396 |                946 |             21636.0 |                     1.0
   (12 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q185Stock discrepancies by warehouse and location type
   • q186Stock discrepancies by product category: handling vs tracking-method issue
   • q187Stock movement magnitude by kind, 2025
   • q190Scale of adjustment-kind stock movements by month, 2025
   • q191Largest individual stock adjustments in 2025
   • q389Trend of stock-count discrepancies within 2025: steady climb or step change?

7. It becomes benchmark query q184

   Changed only where the benchmark needs the same result on every system

   • spellingcast
   • placeholderyear month (2×)
   • determinismtie-breaker: supplier_products primary key
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision; bare NUMERIC cast that only served the rounding removed; intended fraction: integer numerator cast to NUMERIC(18,6) before the division
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (16×)
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (4×)
   The benchmark text, against the submitted SQL

   with cc as (  select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id  select sm.id AS id, sm.variant_id AS variant_id, sm.quantity AS quantity, sm.occurred_at AS occurred_at, sl.warehouse_id AS warehouse_id  from stock_movements sm  join stock_locations sl on sl.id = sm.stock_location_id  where sm.kind = 'cycle_count'    and sm.occurred_at >= timestamp '2025-01-01'    and sm.occurred_at < timestamp '2026-01-01'),supplier_cost as (  select variant_id, currency_code, unit_cost  from (    select sp.variant_id, sp.currency_code, sp.unit_cost,    select sp.variant_id AS variant_id, sp.currency_code AS currency_code, sp.unit_cost AS unit_cost,           row_number() over (             partition by sp.variant_id             order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc             order by case when sp.is_preferred then 0 else 1 end asc NULLS LAST, sp.unit_cost asc NULLS LAST, sp.id asc NULLS LAST           ) as rn    from supplier_products sp    join suppliers s on s.id = sp.supplier_id    where sp.is_preferred = true or s.is_active = true  ) t  where rn = 1),valued as (  select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,         sc.currency_code, sc.unit_cost, er.rate  select cc.id AS id, cc.variant_id AS variant_id, cc.quantity AS quantity, cc.occurred_at AS occurred_at, cc.warehouse_id AS warehouse_id,         sc.currency_code AS currency_code, sc.unit_cost AS unit_cost, er.rate AS rate  from cc  left join supplier_cost sc on sc.variant_id = cc.variant_id  left join exchange_rates er    on er.base_code = 'EUR'   and er.quote_code = sc.currency_code   and er.as_of = cast(cc.occurred_at as date))select  strftime(occurred_at, '%Y-%m') as month,  {{year_month(occurred_at)}} as month,  count(*) as cycle_count_events,  count(distinct warehouse_id) as warehouses_affected,  coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,  coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,  sum(quantity) as net_units_variance,  sum(abs(quantity)) as abs_units_variance,  round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,  round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event  sum(CAST(abs(quantity) * unit_cost / rate AS NUMERIC(18,6))) as estimated_value_eur,  CAST(sum(abs(quantity)) AS numeric(18,6)) / count(*) as avg_abs_units_per_eventfrom valuedgroup by strftime(occurred_at, '%Y-%m')order by monthgroup by {{year_month(occurred_at)}}order by month NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · GizmoSQLHyper · pg_duckdb · PostgreSQL · DataFusion · Firebolt · clickhouse_brewCedarDBClickHouse · chDB

   with cc as (
     select sm.id AS id, sm.variant_id AS variant_id, sm.quantity AS quantity, sm.occurred_at AS occurred_at, sl.warehouse_id AS warehouse_id
     from stock_movements sm
     join stock_locations sl on sl.id = sm.stock_location_id
     where sm.kind = 'cycle_count'
       and sm.occurred_at >= timestamp '2025-01-01'
       and sm.occurred_at < timestamp '2026-01-01'
   ),
   supplier_cost as (
     select variant_id, currency_code, unit_cost
     from (
       select sp.variant_id AS variant_id, sp.currency_code AS currency_code, sp.unit_cost AS unit_cost,
              row_number() over (
                partition by sp.variant_id
                order by case when sp.is_preferred then 0 else 1 end asc NULLS LAST, sp.unit_cost asc NULLS LAST, sp.id asc NULLS LAST
              ) as rn
       from supplier_products sp
       join suppliers s on s.id = sp.supplier_id
       where sp.is_preferred = true or s.is_active = true
     ) t
     where rn = 1
   ),
   valued as (
     select cc.id AS id, cc.variant_id AS variant_id, cc.quantity AS quantity, cc.occurred_at AS occurred_at, cc.warehouse_id AS warehouse_id,
            sc.currency_code AS currency_code, sc.unit_cost AS unit_cost, er.rate AS rate
     from cc
     left join supplier_cost sc on sc.variant_id = cc.variant_id
     left join exchange_rates er
       on er.base_code = 'EUR'
      and er.quote_code = sc.currency_code
      and er.as_of = cast(cc.occurred_at as date)
   )
   select
     strftime(occurred_at, '%Y-%m') as month,
     count(*) as cycle_count_events,
     count(distinct warehouse_id) as warehouses_affected,
     coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
     coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
     sum(quantity) as net_units_variance,
     sum(abs(quantity)) as abs_units_variance,
     sum(CAST(abs(quantity) * unit_cost / rate AS NUMERIC(18,6))) as estimated_value_eur,
     CAST(sum(abs(quantity)) AS numeric(18,6)) / count(*) as avg_abs_units_per_event
   from valued
   group by strftime(occurred_at, '%Y-%m')
   order by month NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 41, Column 38: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "bf0a8a20-fad3-4c41-ac4f-f8323ee41610", "query_lab
   • DataFusionrow count 12 vs 1
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments multiply(abs(__table1.quantity), __table1.unit_cost) Nullable(Decimal(14, 4)) Nullable(size = 0, Decimal64(size = 0), UInt8(size = 0)), __table1.rate Nullable(Decimal(12, 6)) Nul

   Scale factor 1000, c8a.24xlarge, after the last load. q184 on the start page, with every configuration.

8. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Omar's whole session, 11 turns

   • The request, as Omar received it

     Report request from Fatima Zahra (analyst for the Director of Fulfilment Operations):
     
     Title: Scale of stock count discrepancies by month
     Question: How often do physical cycle counts come back different from the stock quantity we had on record, and how large are those differences in units and estimated value, across 2025?
     Grain: calendar month (Jan 2025 - Dec 2025)
     Columns:
     - month (YYYY-MM)
     - cycle_count_events (count of stock_movements rows with kind='cycle_count')
     - warehouses_affected (distinct warehouses with at least one cycle_count event that month)
     - units_short (sum of abs(quantity) where quantity<0, i.e. counted less than recorded)
     - units_over (sum of quantity where quantity>0, i.e. counted more than recorded)
     - net_units_variance (sum of signed quantity; negative = net shrinkage)
     - abs_units_variance (sum of abs(quantity), total magnitude of mismatch)
     - estimated_value_eur (sum of abs(quantity) * unit cost, converted to EUR)
     - avg_abs_units_per_event (abs_units_variance / cycle_count_events, rounded 1dp)
     Details: Source: stock_movements where kind='cycle_count', occurred_at between 2025-01-01 and 2025-12-31 inclusive, across all tenants/warehouses. Join stock_locations on stock_location_id to get warehouse_id for the distinct-warehouse count. Cost basis: join supplier_products on variant_id, preferring is_preferred=true; if no preferred row exists for a variant, use the lowest unit_cost among active (is_active=true) supplier_products rows for that variant; if a variant has no supplier_products row, exclude it from estimated_value_eur but still count it in unit/event columns (note the gap in the report notes). Convert supplier_products.currency_code amounts to EUR using exchange_rates (base_code='EUR', quote_code=that currency, as_of = date of stock_movements.occurred_at): value_eur = value_in_currency / rate. Round estimated_value_eur to whole EUR. Order by month ascending.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Omar calls dry_run

     with cc as (
       select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id
       from stock_movements sm
       join stock_locations sl on sl.id = sm.stock_location_id
       where sm.kind = 'cycle_count'
         and sm.occurred_at >= timestamp '2025-01-01'
         and sm.occurred_at < timestamp '2026-01-01'
     ),
     supplier_cost as (
       select variant_id, currency_code, unit_cost
       from (
         select variant_id, currency_code, unit_cost,
                row_number() over (
                  partition by variant_id
                  order by case when is_preferred then 0 else 1 end asc, unit_cost asc
                ) as rn
         from supplier_products
         where is_preferred = true or is_active = true
       ) t
       where rn = 1
     ),
     valued as (
       select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,
              sc.currency_code, sc.unit_cost, er.rate
       from cc
       left join supplier_cost sc on sc.variant_id = cc.variant_id
       left join exchange_rates er
         on er.base_code = 'EUR'
        and er.quote_code = sc.currency_code
        and er.as_of = cast(cc.occurred_at as date)
     )
     select
       to_char(occurred_at, 'YYYY-MM') as month,
       count(*) as cycle_count_events,
       count(distinct warehouse_id) as warehouses_affected,
       coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
       coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
       sum(quantity) as net_units_variance,
       sum(abs(quantity)) as abs_units_variance,
       round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,
       round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event
     from valued
     group by to_char(occurred_at, 'YYYY-MM')
     order by month

   • The warehouse answers

     STATUS: REJECTED
       - does not compile: Binder Error: Referenced column "is_active" not found in FROM clause!

   • Omar calls dry_run

     with cc as (
       select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id
       from stock_movements sm
       join stock_locations sl on sl.id = sm.stock_location_id
       where sm.kind = 'cycle_count'
         and sm.occurred_at >= timestamp '2025-01-01'
         and sm.occurred_at < timestamp '2026-01-01'
     ),
     supplier_cost as (
       select variant_id, currency_code, unit_cost
       from (
         select sp.variant_id, sp.currency_code, sp.unit_cost,
                row_number() over (
                  partition by sp.variant_id
                  order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc
                ) as rn
         from supplier_products sp
         join suppliers s on s.id = sp.supplier_id
         where sp.is_preferred = true or s.is_active = true
       ) t
       where rn = 1
     ),
     valued as (
       select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,
              sc.currency_code, sc.unit_cost, er.rate
       from cc
       left join supplier_cost sc on sc.variant_id = cc.variant_id
       left join exchange_rates er
         on er.base_code = 'EUR'
        and er.quote_code = sc.currency_code
        and er.as_of = cast(cc.occurred_at as date)
     )
     select
       to_char(occurred_at, 'YYYY-MM') as month,
       count(*) as cycle_count_events,
       count(distinct warehouse_id) as warehouses_affected,
       coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
       coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
       sum(quantity) as net_units_variance,
       sum(abs(quantity)) as abs_units_variance,
       round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,
       round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event
     from valued
     group by to_char(occurred_at, 'YYYY-MM')
     order by month

   • The warehouse answers

     STATUS: REJECTED
       - does not compile: Catalog Error: Scalar Function with name to_char does not exist!

   • Omar calls dry_run

     with cc as (
       select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id
       from stock_movements sm
       join stock_locations sl on sl.id = sm.stock_location_id
       where sm.kind = 'cycle_count'
         and sm.occurred_at >= timestamp '2025-01-01'
         and sm.occurred_at < timestamp '2026-01-01'
     ),
     supplier_cost as (
       select variant_id, currency_code, unit_cost
       from (
         select sp.variant_id, sp.currency_code, sp.unit_cost,
                row_number() over (
                  partition by sp.variant_id
                  order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc
                ) as rn
         from supplier_products sp
         join suppliers s on s.id = sp.supplier_id
         where sp.is_preferred = true or s.is_active = true
       ) t
       where rn = 1
     ),
     valued as (
       select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,
              sc.currency_code, sc.unit_cost, er.rate
       from cc
       left join supplier_cost sc on sc.variant_id = cc.variant_id
       left join exchange_rates er
         on er.base_code = 'EUR'
        and er.quote_code = sc.currency_code
        and er.as_of = cast(cc.occurred_at as date)
     )
     select
       strftime(occurred_at, '%Y-%m') as month,
       count(*) as cycle_count_events,
       count(distinct warehouse_id) as warehouses_affected,
       coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
       coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
       sum(quantity) as net_units_variance,
       sum(abs(quantity)) as abs_units_variance,
       round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,
       round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event
     from valued
     group by strftime(occurred_at, '%Y-%m')
     order by month

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: month, cycle_count_events, warehouses_affected, units_short, units_over, net_units_variance, abs_units_variance, estimated_value_eur, avg_abs_units_per_event

   • OmarQuery compiles and the output columns match the requested report exactly.
   • Omar calls submit

     with cc as (
       select sm.id, sm.variant_id, sm.quantity, sm.occurred_at, sl.warehouse_id
       from stock_movements sm
       join stock_locations sl on sl.id = sm.stock_location_id
       where sm.kind = 'cycle_count'
         and sm.occurred_at >= timestamp '2025-01-01'
         and sm.occurred_at < timestamp '2026-01-01'
     ),
     supplier_cost as (
       select variant_id, currency_code, unit_cost
       from (
         select sp.variant_id, sp.currency_code, sp.unit_cost,
                row_number() over (
                  partition by sp.variant_id
                  order by case when sp.is_preferred then 0 else 1 end asc, sp.unit_cost asc
                ) as rn
         from supplier_products sp
         join suppliers s on s.id = sp.supplier_id
         where sp.is_preferred = true or s.is_active = true
       ) t
       where rn = 1
     ),
     valued as (
       select cc.id, cc.variant_id, cc.quantity, cc.occurred_at, cc.warehouse_id,
              sc.currency_code, sc.unit_cost, er.rate
       from cc
       left join supplier_cost sc on sc.variant_id = cc.variant_id
       left join exchange_rates er
         on er.base_code = 'EUR'
        and er.quote_code = sc.currency_code
        and er.as_of = cast(cc.occurred_at as date)
     )
     select
       strftime(occurred_at, '%Y-%m') as month,
       count(*) as cycle_count_events,
       count(distinct warehouse_id) as warehouses_affected,
       coalesce(sum(abs(quantity)) filter (where quantity < 0), 0) as units_short,
       coalesce(sum(quantity) filter (where quantity > 0), 0) as units_over,
       sum(quantity) as net_units_variance,
       sum(abs(quantity)) as abs_units_variance,
       round(sum(abs(quantity) * unit_cost / rate), 0) as estimated_value_eur,
       round(sum(abs(quantity))::numeric / count(*), 1) as avg_abs_units_per_event
     from valued
     group by strftime(occurred_at, '%Y-%m')
     order by month

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • OmarSubmitted. One thing worth flagging to Fatima: `supplier_products` itself has no `is_active` flag — I treated "active supplier_products rows" as rows coming from a supplier with `suppliers.is_active = true`, which is the closest match in the schema. That's noted in the submit note as well.

   Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Omar's transcript (287 KB) · Fatima Zahra's transcript (935 KB) · Tomasz Nowak's transcript (261 KB) · the night's plan (7 KB) · the night's reports (24 KB) · the week's memo (3 KB)